amazon-research / datatuner
Code related to "Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity" paper
Summary
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| Main Code: 4,158 LOC (35 files) = PY (98%) + YML (1%) Secondary code: Test: 96 LOC (2); Generated: 0 LOC (0); Build & Deploy: 272 LOC (9); Other: 911 LOC (25); |
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| Duplication: <1% | |||
| File Size: 0% long (>1000 LOC), 36% short (<= 200 LOC) | |||
| Unit Size: 41% long (>100 LOC), 27% short (<= 10 LOC) | |||
| Conditional Complexity: 45% complex (McCabe index > 50), 25% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (9 components) | ||
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10 months old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
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Features of interest:
TODOs
3 files |
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Latest commit date: 2021-07-29
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